The exact distribution and density functions of the stein-type estimator for normal variance

DOI10.1080/03610929308831190zbMath0800.62072OpenAlexW2017663379MaRDI QIDQ4275849

Kazuhiro Ohtani

Publication date: 31 January 1994

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610929308831190

Mathematics Subject Classification ID

Point estimation (62F10) Exact distribution theory in statistics (62E15)

Related Items (6)

The density function and the MSE dominance of the pre-test estimator in a heteroscedastic linear regression model with omitted variables ⋮ The exact distribution and density functions of a pre-test estimator of the error variance in a linear regression model with proxy variables ⋮ The relationship between the improvement on the point estimation and the improvement on the interval estimation for the disturbance variance in a linear regression model ⋮ The neyman accuracy and the wolfowitz accuracy of the stein type confidence interval for the disturbance variance ⋮ Stein type confidence interval of the disturbance variance in a linear regression model with multivariate student-t distributed errors ⋮ Exact distribution of a pre-test estimator for regression error variance when there are omitted variables.

Cites Work

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• Optimal levels of significance of a pre-test in estimating the disturbance variance after the pre-test for a linear hypothesis on coefficients in a linear regression
• Improved confidence intervals for a normal variance
• Estimating the error variance in regression after a preliminary test of restrictions on the coefficients
• Improved estimation of the disturbance variance in a linear regression model
• Improved invariant confidence intervals for a normal variance
• Pre-testing for linear restrictions in a regression model with spherically symmetric disturbances
• Risk behavior of variance estimators in multivariate normal distribution
• Improving on equivariant estimators
• Inadmissibility of the usual estimator for the variance of a normal distribution with unknown mean
• Estimation of the variance in a normal population after the one-sided pre-test for the mean
• Bayesian Estimation and Prediction Using Asymmetric Loss Functions
• Inadmissibility of the Usual Estimators of Scale parameters in Problems with Unknown Location and Scale Parameters

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